Surgical procedure planning system with multiple feedback loops

ABSTRACT

A surgical procedure planning system and method that uses multiple feedback loops to optimize creation or design of future surgical preoperative plans.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefits of priority from U.S. ProvisionalApplication No. 62/459,891, filed on Feb. 16, 2017, the entirety ofwhich is incorporated herein by reference.

BACKGROUND

In surgical disciplines, surgical procedures are affected by theuncertainty associated with complex variability between patient'sanatomy, injury disease state, and functionality. In other words, eventhough a defined surgical procedure on a patient may be a generallydefined procedure with a defined order of steps for a defined anatomicalpatient part, uncertainty almost always exists because patient's anatomyat least slightly differs, each patient's injury or disease state atleast slightly differs, and the functionality of each patient's anatomyat least slightly differs. This uncertainty often arises in the abilityto correctly create or design a preoperative plan to produce the optimalresult for a specific patient. This uncertainty over the complexvariability and the hierarchy of prioritizing preoperative plan creationor design decisions is one of the greatest sources of potential errorand risk associated with creating a preoperative plan.

In creating a preoperative plan, many issues typically need to beconsidered, addressed, or determined. In every surgical specialty, thereare general rules that typically guide how the preoperative planaddresses these issues. For example, the preoperative plan for a kneereplacement typically includes an identification of the anatomic axis ofthe femur (i.e., the thigh bone) and the mechanical (weight bearing)axis. The preoperative plan also typically includes an assessment of therotational alignment for the femur that affects stability of the kneeand the motion of the kneecap (i.e., the patella). The preoperative planalso typically includes an evaluation of the size, orientation, andproportions of the bones. The preoperative plan also typically includesa proposed depth of bone resection on the femur that relates tobalancing ligament tension, soft tissue balance, stability to the knee,and a component that fits the femur without undue irritation to thesurrounding tissue. The preoperative plan also typically includes adetermination of tibial (i.e., shin bone) axis and the angle of the bonepreparation of the tibia that similarly effects balancing ligamenttension, soft tissue balance, stability to the knee and a component thatfits the tibia without undue irritation to the surrounding tissue. Thepreoperative plan also typically includes chosen sizes for each of thecomponents and assignments for the three-dimensional placement of thecomponents. The components are placed with these and potentially manymore considerations on every patient to facilitate a safe procedure andpostoperative course and as well as maximal function of the knee.

One of the known ways to address preoperative plan creation and designuncertainty has been through the aggregate prediction based on one ormore known preoperative plan sets. Each of these known preoperative plansets include a plurality of different individual preoperative plans forthe same surgical procedure (such as a left knee replacement or a righthip replacement), where each individual preoperative plan of the setprovides a unique preoperative plan based on automated or software basedanalysis of complex anatomy and function of the particular surgical sitefor the particular patient. The aggregate preoperative plan obtainedfrom or based on such a preoperative plan set can in certain instancesprovide a better overall predictive performance and can exhibit lessbias than the preoperative plan set's individual constituents becausethe aggregate is derived from a weighted combination of all thepreoperative plan set members.

While such aggregated preoperative plans provide more accurate estimatesfor prediction and forecasting for future individual patientpreoperative plans, these aggregates do not help automated and/or manualpreoperative plan creators or designers refine and improve theindividual preoperative plans (such as for robotically assisted surgicalprocedures) that initially make up or form the preoperative plan set.

Accordingly, there is a need to solve these problems and to providebetter preoperative plans that can be aggregated and used to create ordesign future preoperative plans.

SUMMARY

Various embodiments of the present disclosure solve the above problemsby providing a surgical procedure planning system and method withmultiple feedback loops for optimizing future surgical preoperativeplans. Various embodiments of the present disclosure provide a systemand method that utilizes the benefits of certain aggregation techniquesto provide feedback to preoperative plan creators or designers, so thatthey can better understand various factors and hierarchy to improve theindividual preoperative plans that form or are used as preoperativeplans in or for development of future preoperative plan sets, and thusfor the development of future enhanced individual preoperative plans forindividual patients.

While aggregating preoperative plans for sets of similar anatomicalconditions can assist in producing models for future preoperative plans,such steps do not by themselves fully optimize outcomes. Thus, invarious embodiments of the present disclosure, the system and methodfacilitates modeling done on aggregated selected preoperative plansbased on existing rules which prioritize decisions made in the planningprocess. And, in various other embodiments of the present disclosure,the system and method facilitates modeling done on aggregatepreoperative plans based on one or more surgeon selected prioritizeddesired outcomes that cause the system and method to determine which ofthe system stored preoperative plans to select for aggregation based onthe patient outcomes that resulted from the use of those storedpreoperative plans. For example, in certain such embodiments, the systemand method determines which of the system stored preoperative plans toselect for aggregation by selecting one or more specific storedpreoperative plans that needed the fewest revisions to their respectiveinitial preoperative plans (on which they were based) and that resultedin the best patient reported outcomes. In certain such embodiments, thesystem and method uses suitable machine learning to optimize anddetermine a new hierarchy of decisions for preoperative planning forsubsequent patients who share specific common features tracked by thesystem and method to best achieve the desired outcome. In variousembodiments of the present disclosure, the system and method employsboth of these methods.

In some examples, the systems and methods described herein may generateoperative plans, such as preoperative or intraoperative plans, based onan automated calculation using measurements of distances from anatomicallandmarks. For example, the operative plan may include positioning afemoral prosthetic component with respect to a distance from the medialor lateral condyle, and a tibial prosthetic component may be positionedbased upon a distance from the tibial tuberosity. In some examples, thepreoperative plan may include specific dimensions of anatomicalstructures of a patient, and/or ranges of measurements, such asanatomical measurements, may be incorporated into an operative plan orprovided as a reference for a surgeon. Anatomical distances may bemeasured by a surgical robot, a health care provider, electronic accessdevice, imaging system, or any other means before or during the surgicalprocedure, and the system or method may generate suggested prostheticpositioning data and/or an operative plan based on the anatomicaldistances measured. In some examples, the system or method may generatesuggested prosthetic positioning data, bone preparation data such aspositioned where the surgeon should cut the bone, and/or an operativeplan based (1) age, (2) sex, (3) nationality (to take into account theknown distinctly different patterns of anatomy in different ethnicgroups), (4) bone density, (5) height, (6) weight, (7) BMI, (8) lowerextremity mechanical alignment, (9) lower extremity anatomicalalignment, (10) femoral articular surface angle, (11) tibial articularsurface angle, (12) mechanical axis alignment strategy, (13) anatomicalalignment strategy, (14) natural knee alignment strategy, (15) femoralbowing, (16) tibial bowing, (17) patello-femoral alignment, (18) coronalplane deformity, (19) coronal plane deformity that can be passivelycorrectable, (20) sagittal plane deformity, (21) extension motion, (22)flexion motion, (23) ACL ligament intact, (24) PCL ligament intact, (25)knee motion in all three planes during active and passive range ofmotion in the joint; (26) three dimensional size, proportions andrelationships of joint anatomy in both static and motion, (27) height ofthe joint line, (28) lateral epicondyle, (29) medial epicondyle, (30)lateral femoral metaphyseal flare, (31) medial femoral metaphysealflare, (32) proximal tibio-fibular joint, (33) tibial tubercle, (34)coronal tibial diameter, (35) femoral interepicondylar diameter, (36)femoral intermetaphyseal diameter, (37) sagittal tibial diameter, (38)posterior femoral condylar offset-medial and lateral, (39) lateralepicondyle to joint line distance, and/or (40) tibial tubercle to jointline distance.

The system and method of various embodiments of the present disclosureenables or facilitates creating or designing preoperative plans usingone or more specific feedback loops not previously used. In one suchembodiment, the method generally includes: (a) selecting a plurality ofpreoperative plans for creating or designing a future preoperative plan;(b) aggregating the results of or feedback based on each actual use ofeach of the selected preoperative plans; (c) analyzing each preoperativeplan compared to the aggregate result to obtain reliable or comparativeinformation; and (d) providing the resulting information back for use inmodifying each of the plurality of preoperative plans to automaticallyand/or manually create or design more accurate initial preoperativeplans through this feedback loop.

The system and method of various embodiments of the present disclosureincludes selecting an optimal electronic or digital initial preoperativeplan (which can be locally based or stored, server based or stored,cloud based or stored, or otherwise electronically or digitally based orstored) for a specific surgical procedure and creating a physical orvirtual model that represents the application of the initialpreoperative plan on an individual patient's anatomy. The system andmethod then electronically provides or releases this selected initialpreoperative plan to the surgeon. After this initial preoperative planhas been provided or released to the surgeon for review and to make anychanges, and before the actual surgical procedure for the individualpatient, the system and method receives any changes to the initialpreoperative plan from the surgeon, and incorporates these changes toautomatically and/or manually create a secondary preoperative plan and asecondary physical or virtual model. In some examples, changes to thepreoperative plan may be completed after initiating the surgicalprocedure. For example, a surgeon may have completed a cut into thepatient's tibia, and as a result of the surgeon's or a surgical robot'sevaluation of the patient's cartilage the preoperative plan is changedto account for the evaluation of the patient's cartilage. In someexamples, the type of surgery may be changed in the operative plan basedon data acquired during the surgical operation, such as a partial kneereplacement plan may be updated to a total knee replacement plan basedon data acquired during the surgical operation.

In some examples, changes to the operative plan may occur after aninitial or secondary preoperative plan has been provided or released tothe surgeon for use in a surgical procedure for the individual patient.For example, a surgeon, a robotic system or a surgical robot workingindependently or together may collect new data, such as soft tissuetension, ligament integrity, range of motion of the joint or quality ofthe articular cartilage prior to completing the surgical procedure, andincorporate this new data into the operative plan, such as byautomatically creating a new tertiary preoperative plan and/or atertiary physical or virtual model to be used on this individual patientduring this same operation.

The system and method of various embodiments of the present disclosureincludes providing this received information on changes to the initialpreoperative plan and physical or virtual model in a first automatedelectronically transmitted feedback loop (that may employ the use of anytype of conventional server, physical flash drives, Internet wired orwireless data transfer, data transfer through physical server, datatransfer through cloud-based servers, and/or otherwise electronically ordigitally based systems) to enable the system and method toautomatically and/or manually create or design more accurate or enhancedfuture initial preoperative plans and enhanced physical or virtualmodels for subsequent patients who share specific common featurestracked by the system and method. This is the first type of automatedfeedback loop that the system and method of the present disclosureprovides to improve surgical procedure preoperative plans that aresubsequently used as part of preoperative plan sets to create aggregatedpreoperative plans.

After the actual surgical procedure for the individual patient takesplace, the system and method of various embodiments of the presentdisclosure also receives any further changes to the secondarypreoperative plan and model that are actually implemented in theoperating room. The system and method includes providing this furtherreceived information on changes to the secondary preoperative plan andphysical or virtual model in a second automated electronic feedback loop(that may employ any type of conventional server, the use of physicalflash drives, Internet wired or wireless data transfer, data transferthrough physical server, data transfer through cloud-based servers,and/or otherwise electronically or digitally based systems) to enablethe system and method to automatically and/or manually create or designmore accurate or enhanced future initial preoperative plans and enhancedphysical or virtual models for subsequent patients who share specificcommon features tracked by the system and method. This is the secondtype of automated feedback loop that the system and method of thepresent disclosure provides to improve surgical procedure preoperativeplans that are subsequently used as part of preoperative plan sets tocreate aggregated preoperative plans.

After the actual surgical procedure for the individual patient iscompleted and after the surgeon obtains actual feedback from the patientregarding the actual results of the surgery and the patient's recovery,the system and method of various embodiments of the present disclosure,also receives such information. This information includes a wide rangeof different information such as patient clinical outcome information onthe results of the operation, including imaging techniques, personalhealth trackers, wearable sensors, biometric sensors, patient reportedoutcomes and outcome measurement tools.

The system and method of various embodiments of the present disclosureincludes providing this further received patient result information in athird automated electronic feedback loop (that may employ the use of anytype of conventional server, physical flash drives, Internet wired orwireless data transfer, data transfer through physical server, datatransfer through cloud-based servers, and/or otherwise electronically ordigitally based systems) to enable the system and method toautomatically and/or manually create or design more accurate or enhancedfuture initial preoperative plans and enhanced physical or virtualmodels for subsequent patients who share specific common featurestracked by the system and method. This is the third type of automatedfeedback loop that the system and method of the present disclosureprovides to improve surgical procedure preoperative plans that aresubsequently used as part of preoperative plan sets to create aggregatedpreoperative plans.

According to one aspect of the present disclosure, acomputer-implemented method for optimizing a future surgicalpreoperative plan is disclosed. The computer-implemented method mayinclude providing, via a server, a selection of an optimal electronicinitial preoperative plan for a robotic surgical procedure. The surgicalprocedure may include implanting an implant and/or prosthesis into apatient with the assistance of a robot. The method may also includeelectronically, via a server and an electronic access device, providingan electronic initial preoperative plan to a surgical robot. The methodmay also include receiving, prior to the surgical procedure and via anelectronic access device, information regarding the initial preoperativeplan from a surgeon or a surgical robot. In addition, the method mayinclude executing an algorithm, via a server, to create a secondarypreoperative plan incorporating information from the surgeon or surgicalrobot, and providing, via a server and an electronic access device, asecondary preoperative plan to a surgical robot. The method may furtherinclude storing in a server and/or an electronic access deviceinformation from the surgical robot and/or the surgeon for use increating a preoperative plan for a subsequent patient who shares atleast one common feature with the individual patient, and receiving,during the surgical procedure for the individual patient and via theelectronic access device and/or the surgical robot, patient surgicalinformation. In addition, the method may include storing in a serverand/or an electronic access device patient surgical information for usein creating a preoperative plan for a subsequent patient who shares atleast one common feature with the individual patient. Patient surgicalinformation may include preoperative, intraoperative, and/orpostoperative data.

In other aspects of the present disclosure, the computer-implementedmethod may include one or more of the features below. The method mayinclude executing an algorithm using the patient surgical information,via a server and during a surgical procedure for the individual patient,to create a tertiary preoperative plan, and may further includeproviding, via a server and an electronic access device, the tertiarypreoperative plan to the surgical robot. The method may includedisplaying, via an electronic access device or the surgical robot, thetertiary preoperative plan to the surgeon during the surgical procedure.The method may also include storing in the server and/or the electronicaccess device the tertiary preoperative plan for use in creating apreoperative plan for a subsequent patient who shares at least onecommon feature with the individual patient. The surgical procedure maybe an osteotomy procedure. The surgical robot may include a roboticassisted device, a computer assisted device, an autonomous roboticdevice, or a digital surgery platform. The method may further includereceiving, after the surgical procedure and via the server, the surgicalrobot, or the electronic access device, information regarding theresults of the surgical procedure from the patient; executing analgorithm, via the server, surgical robot, or electronic access device,to create an updated preoperative plan incorporating the informationregarding the results of the surgical procedure from the patient; andstoring in the server and/or the electronic access device the updatedpreoperative plan for use in creating a preoperative plan for asubsequent patient who shares at least one common feature with theindividual patient. The method may also include executing an algorithm,via the server, to create a virtual model of the patient's anatomyincorporating the information from the surgeon or surgical robot andrepresenting an application of the secondary preoperative plan;providing, via the server and the electronic access device, the virtualmodel to the surgical robot or the electronic access device; displaying,via the electronic access device or the surgical robot, the virtualmodel to the surgeon during the surgical procedure; and storing in theserver and/or the electronic access device the virtual model for use increating a virtual model for a subsequent patient who shares at leastone common feature with the individual patient.

The method may also include displaying a virtual model to a surgeonincluding displaying an augmented reality of the surgical procedure. Thealgorithm may include the aggregation of preoperative plans, surgicalmeasurements, and patient outcomes stored on the server from priorsurgical procedures involving patients who share at least one commonfeature with the individual patient. The algorithm may include analysisof preoperative plans, surgical measurements, and patient outcomesstored on the server from prior surgical procedures involving patientswho shares at least one common feature with the individual patient,wherein the analysis includes using Statistical Natural LanguageProcessing (SNLP), Bayesian aggregation, Machine learning, artificialintelligence, self-learning, Neural Networks, Deep recurrent neuralnetworks, basic reinforcement learning, and deep reinforcement learning.The patient surgical information may include the patient's range ofmotion; intra-operative data on the patient's soft tissue conditions,angular deformities, flexion contracture, recurvatum, limited flexion,varus, valgus, joint flexibility, laxity, or ability to passivelycorrect a deformity. The information regarding the results of a surgicalprocedure from a patient may include electronic medical records (EMR)data, data related to the knee society clinical rating system (KSS),data related to the Western Ontario and McMaster Universitiesosteoarthritis index (WOMAC), HOOS scores, KOOS scores, Harris HipScores, SF-12 scores, Sf-36 scores, wearable sensor data, patient selfreported data, or imaging data from medical images of the patient. Themethod may further include providing, via the server and the electronicaccess device, suggestions to modify a secondary preoperative plan to asurgical robot during a surgical procedure based on patient surgicalinformation. The patient surgical information may include data relatingto the patient's soft tissue, range of motion, deformity, measuredresection, mechanical axis, natural knee alignment, or gap balancing.The method may also include selecting an optimal electronic initialpreoperative plan for a robotic surgical procedure includes receivingand analyzing, via the server, genomic data of the patient or surfacemapping data from images of the patient's anatomy. The method may alsoinclude displaying, via an electronic access device or a surgical robot,a real-time image of the patient's anatomy to the surgeon during thesurgical procedure.

The patient surgical information may include medical imaging data of thepatient. In some examples, the patient information regarding the initialpreoperative plan from the surgeon or surgical robot may include atleast one of the patient's femoral articular surface angle, tibialarticular surface angle, femoral bowing, tibial bowing, patella-femoralalignment, coronal plane deformity, sagittal plane deformity, extensionmotion, flexion motion, ACL ligament, PCL ligament, knee motion in allthree planes during active and passive range of motion in the joint,height of the joint line, lateral epicondyle, medial epicondyle, lateralfemoral metaphyseal flare, medial femoral metaphyseal flare, proximaltibio-fibular joint, tibial tubercle, coronal tibial diameter, femoralinterepicondylar diameter, femoral intermetaphyseal diameter, sagittaltibial diameter, posterior femoral condylar offset-medial and lateral,lateral epicondyle to joint line distance, and tibial tubercle to jointline distance. In some examples, the surgical procedure may include theplacement of an implant of autologous tissue, allograft tissue, orsynthetic materials; a cardiac, orthopedic, neurologic, urologic,ophthalmologic, obstetric and gynecologic, otolaryngology, plastic, orgeneral surgical procedure; and/or a valve replacement or atranscatheter aortic valve replacement (TAVR) procedure.

In other aspects, a computer-implemented method for optimizing a futuresurgical preoperative plan may include providing, via a server, aselection of an optimal electronic initial preoperative plan for asurgical procedure. The surgical procedure may include implanting animplant and/or prosthesis into a patient with the assistance of a robot.The method may also include creating, via a server, a patient specificimplant design and/or prosthesis design; electronically, via the serverand an electronic access device, providing the electronic initialpreoperative plan and the implant design and/or prosthesis design to asurgical robot; receiving, prior to the surgical procedure and via theelectronic access device or surgical robot, information regarding theinitial preoperative plan and/or the implant design and/or prosthesisdesign from the surgical robot; executing an algorithm, via the server,to create a secondary preoperative plan and a secondary implant designand/or prosthesis design incorporating the information from the surgicalrobot; providing, via the server and the electronic access device, thesecondary preoperative plan and the secondary implant design and/orprosthesis design to the surgeon or surgical robot; storing in theserver and/or the electronic access device the information from thesurgical robot for use in creating a preoperative plan and/or implantdesign and/or prosthesis design for a subsequent patient who shares atleast one common feature with the individual patient; identifying animplant and/or prosthesis for use in the surgical procedure based on thesecondary implant design and/or the secondary prosthesis design; andstoring in the server and/or the electronic access device the built orselected implant and/or prosthesis design for use in creating apreoperative plan and/or implant and/or prosthesis design for asubsequent patient who shares at least one common feature with theindividual patient.

In other aspects, the method for optimizing a future surgicalpreoperative plan may include creating a patient specific implant designand/or prosthesis design including matching the patient's arc ofcurvature of the native femoral condyle with an arc of curvature of theimplant and/or prosthesis. The method may also include creating animplant design and/or prosthesis design using medical images of thepatient and an algorithm that includes the aggregation of preoperativeplans, surgical measurements, medical images, and patient outcomesstored on the server from prior surgical procedures involving patientswho shares at least one common feature with the individual patient.

In other aspects of the present disclosure, a computer-implementedmethod for optimizing a future surgical preoperative plan is disclosed.In some examples, the method may include providing, via a server, aselection of an optimal electronic initial preoperative plan for asurgical procedure. The surgical procedure may include implanting animplant and/or prosthesis into a patient with the assistance of a robot.The method may also include creating, via the server, a patient specificsurgical instrument design; electronically, via the server and anelectronic access device, providing the electronic initial preoperativeplan and the surgical instrument design to a surgical robot; receiving,prior to the surgical procedure and via an electronic access device orsurgical robot, information regarding the initial preoperative planand/or the surgical instrument design; executing an algorithm, via theserver, to create a secondary preoperative plan and a secondary surgicalinstrument design incorporating the information; providing, via theserver and the electronic access device, the secondary preoperative planand the secondary surgical instrument design to the surgeon or surgicalrobot; storing in the server and/or the electronic access device theinformation for use in creating a preoperative plan and/or surgicalinstrument design for a subsequent patient who shares at least onecommon feature with the individual patient; identifying a surgicalinstrument for use in the surgical procedure based on the secondarysurgical instrument design; and storing in the server and/or theelectronic access device the built or selected surgical instrumentdesign for use in creating a preoperative plan and/or surgicalinstrument design for a subsequent patient who shares at least onecommon feature with the individual patient.

Other objects, features and advantages of the present invention will beapparent from the following detailed disclosure, taken in conjunctionwith the accompanying sheets of drawings, wherein like referencenumerals refer to like parts.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of theinvention, as claimed. As used herein, the terms “comprises,”“comprising,” or any other variation thereof, are intended to cover anon-exclusive inclusion, such that a process, method, article, orapparatus that comprises a list of elements does not necessarily includeonly those elements, but may include other elements not expressly listedor inherent to such process, method, article, or apparatus. The term“exemplary” is used in the sense of “example,” rather than “ideal.”

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 shows a system work-flow depicting an information feedback loopdriving preoperative plan creation or design, in accordance with oneexample embodiment of the present disclosure.

FIG. 2 is a flowchart of a method of creating or designing preoperativeplans using a feedback loop, in accordance with one example embodimentof the present disclosure.

FIG. 3 shows a workflow depicting a feedback loop disclosing the variousstages of iteration available to a given preoperative plan and how eachiteration can be sent back to the aggregate independently or as part ofa complete workflow.

DETAILED DESCRIPTION

One example embodiment of the present disclosure provides a roboticallyassisted surgical procedure planning system and method with multiplefeedback loops for automatically optimizing future robotically assistedsurgical preoperative plans. This example system and method includes:(a) determining or selecting an optimal electronic initial preoperativeplan for a specific robotically assisted surgical procedure and creatinga virtual model that represents the application of that initialpreoperative plan on an individual patient; (b) electronically providingthis selected initial preoperative plan to the surgeon; (c) before theactual surgical procedure for the individual patient takes place, thesurgeon reviews the initial preoperative plan and can choose to makechanges to that preoperative plan, receiving any changes to the initialpreoperative plan from the surgeon; (d) using these received changes toautomatically create a secondary preoperative plan and a secondaryvirtual model (as part of this first automated feedback loop); and (e)electronically providing this secondary preoperative plan and model tothe surgeon for use during the robotically assisted surgical procedure.This secondary plan is automatically sent via an electronic feedbackloop back for storage in the system to be used as part of a preoperativeplan set to create an aggregated preoperative plan for a subsequentpatient who shares one or more specific common features with theindividual patient. During the period when the actual surgical procedurefor the individual patient takes place, the system electronicallyregisters or stores certain individual patient related data found orrealized during the initial parts of the surgical procedure oroperation, such as range of motion, ligament laxity and deformity. Thisadditional data may cause one or more further changes to the secondarypreoperative plan and model in real time that are performed either bythe system automatically or by the surgeon, creating a tertiary plan.This tertiary plan is then subsequently implemented during the rest ofthe actual surgical procedure on the individual patient in the operatingroom. The tertiary preoperative plan is also automatically sent via anelectronic feedback loop back for storage in the system to be used aspart of a preoperative plan set to create an aggregated preoperativeplan for a subsequent patient who shares one or more specific commonfeatures with the individual patient. In various embodiments, thisexample system and method further includes: (i) after the surgeonobtains actual feedback from the patient regarding the actual results ofthe surgery and the individual's patients recovery, receiving individualpatient clinical outcome information on the results of the surgicalprocedure; and (ii) automatically using this further received patientclinical outcome information in a further automated feedback loop toautomatically create an enhanced future initial preoperative plan and anenhanced virtual model for a subsequent patient who shares one or morespecific common features with the individual patient. In certain suchembodiments, the system and method can use this further received patientclinical outcome information with integrated connections to surgicaloutcome registries, patient universal health records, or data fromphysician or hospital electronic medical record systems to enhance thefuture individual preoperative plans.

Another example embodiment of the present disclosure provides a patientspecific instrumentation system surgical procedure planning system andmethod with multiple feedback loops for automatically optimizing futurepatient specific instrumentation system surgical preoperative plans.This example system and method includes: (a) determining or selecting anoptimal electronic initial preoperative plan for a specific patientspecific instrumentation system surgical procedure and creating avirtual model that represents the application of that initialpreoperative plan on an individual patient; (b) electronically providingthis selected initial preoperative plan to the surgeon; (c) before theactual surgical procedure for the individual patient takes place,receiving any changes to the initial preoperative plan from the surgeon;(d) using these received changes to automatically create a secondarypreoperative plan and a secondary virtual model (as part of this firstautomated feedback loop); (e) electronically providing this secondarypreoperative plan and model for fabrication of a patient specificinstrumentation system cutting block instrument; and (f) during theactual surgical procedure for the individual patient takes place, thesystem and method registering certain data found during the initialparts of the operation, such as range of motion, ligament laxity anddeformity. This additional data may cause further changes to thesecondary plan and model, performed either by the system automaticallyor by the surgeon, creating a tertiary plan. This tertiary plan is thenimplemented during the rest of the actual surgical procedure on theindividual patient in the operating room; and (g) the tertiary plan isautomatically sent via a feedback loop to the system to be stored andused as part of a preoperative plan set to create an aggregatedpreoperative plan for a subsequent patient who shares one or morespecific common features with the individual patient. In variousembodiments, this example system and method further includes: (h) afterthe surgeon obtains actual feedback from the patient regarding theactual results of the surgery and the individual's patients recovery,receiving individual patient clinical outcome information on the resultsof the surgical procedure; and (i) automatically using this furtherreceived patient clinical outcome information in a further automatedfeedback loop to automatically create an enhanced future initialpreoperative plan and an enhanced virtual model for a subsequent patientwho shares one or more specific common features with the individualpatient.

Another example embodiment of the present disclosure provides a surgicalprocedure planning system and method with multiple feedback loops forautomatically optimizing future surgical preoperative plans which createpatient specific (such as custom) implants and prostheses. This examplesystem and method includes: (a) determining or selecting an optimalelectronic initial preoperative plan for a specific surgical procedureand creating a virtual model that represents the application of thatinitial preoperative plan on an individual patient, creating a patientspecific (custom) implant/prosthesis design; (b) electronicallyproviding this selected initial preoperative plan to the surgeon; (c)before the actual surgical procedure for the individual patient takesplace, receiving any changes to the initial preoperative plan from thesurgeon; (d) using these received changes to automatically create asecondary preoperative plan and a secondary virtual model (as part ofthis first automated feedback loop); (e) electronically providing thissecondary preoperative plan, model and patient specific (custom)implant/prosthesis design to the surgeon for use during surgicalprocedure; (f) during the actual surgical procedure for the individualpatient, the system registers certain data found during the initialparts of the operation, such as range of motion, ligament laxity anddeformity. This additional data may cause further changes to thesecondary plan and model, performed either by the system automaticallyor by the surgeon, creating a tertiary plan. This tertiary plan is thenimplemented during the rest of the actual surgical procedure on theindividual patient in the operating room; and (g) the tertiary plan isautomatically sent via a feedback loop to the system to be used as partof a preoperative plan set to create an aggregated preoperative plan fora subsequent patient who shares one or more specific common featureswith the individual patient. In various embodiments, this example systemand method further includes: (h) after the surgeon obtains actualfeedback from the patient regarding the actual results of the surgeryand the individual's patients recovery, receiving individual patientclinical outcome information on the results of the surgical procedure;and (i) automatically using this further received patient clinicaloutcome information in a further automated feedback loop toautomatically create an enhanced future initial preoperative plan and anenhanced virtual model for a subsequent patient who shares one or morespecific common features with the individual patient.

In one example embodiment, the system and method automatically selects aplurality of different stored preoperative plans from an electronicpreoperative plan database for creating or designing an initialpreoperative plan for a total knee replacement for an individualpatient. The system and method automatically aggregates the results ofthe plurality of selected preoperative plans into an aggregate toproduce an optimal initial preoperative plan for the individualpatient's knee requiring surgery given a set a preoperative variablesregarding the individual patient. The system and method analyzing eachselected preoperative plan compared to the aggregate resultingpreoperative plan to obtain comparative information. This comparativeinformation includes one or more comparisons of each preoperative planto the resulting aggregate preoperative plan. The system and methodelectronically provides the surgeon this selected initial preoperativeplan for the knee surgery. The system and method electronically enablesthe surgeon to input proposed changes. Those changes to this initialpreoperative plan are sent back to the system using a first feedbackloop to be included in future aggregation, modeling and optimization forfuture patient preoperative plans. The surgeon then uses thepreoperative plan on their patient and in doing so will record digitallyintra-operative information such as: (a) range of motion data; (b) dataon angular deformity of the joint; and/or (c) data regarding the abilityto passively correct the angular deformity. The system and method usesthis intra-operative information to create a tertiary preoperative plan,and this information (both the intra-operative information added and thechanges to the preoperative plan that the information produced) are sentback via the feedback loop to be used in future preoperative plans onsubsequent patients who share specific common features tracked by thesystem and where similar intra-operative information is recorded. Inthis subsequent situation, the system and method uses the aggregation,modeling, and optimization of previous pre-operative plans that hadsimilar intra-operative information recorded to optimizeintra-operatively on subsequent patient's preoperative plan and makesuggested changes. The system and method electronically provides thesurgeon this tertiary preoperative plan for use during the surgicalprocedure. The system and method uses these further received changesactually implemented during that actual surgical procedure toautomatically create a tertiary preoperative plan as part of this secondautomated feedback loop. The system and method can then use thistertiary preoperative plan as part of a preoperative plan set to createan aggregated preoperative plan for a subsequent patient who shares oneor more specific common features with the individual patient. After theactual surgical procedure and after a certain amount of time of patientrecovery, the system and method electronically enables the surgeon toinput further information regarding the patient's knee and recoveryprogress in a further feedback loop. This information can for exampleinclude: patient outcome measurement tools (such as the Knee SocietyScore), accessing surgical patient outcome registries, and/or physicianor hospital electronic medical records. The system and method uses thisfurther received information to automatically revise or create a fourthpreoperative plan as part of this third automated feedback loop. Thesystem and method can then use this fourth preoperative plan as part ofa preoperative plan set to create an aggregated preoperative plan for asubsequent patient who shares one or more specific common features withthe individual patient.

In various embodiments, the system and method of the present disclosure,after identifying a similar set of aggregated data from those similarpreoperative plans, uses its analysis to look at edits and revisionsthat had to be done to the aggregated preoperative plans and optimizesto create one or more improved preoperative plans for future use. Thesystem and method further enhances this process by analyzing clinicaloutcome of those aggregated preoperative plans to provide furtherrefinements to the initial preoperative plan being produced for thesubsequent patient.

In another example embodiment, a patient has decided to have a totalknee replacement performed. The surgeon caring for this patient prefersto use the existing rules for establishing their preoperative plan. Aspart of the preparation for surgery, the patient has x-rays performed onthe limb and a CT Scan. Those electronic imaging studies undergosegmentation to provide identification of boundaries and features aswell as identifying specific anatomical features producing an electronicplan. In this example, this can be done by suitable auto-segmentationsoftware or can be performed partially or fully manually. This can bepart of the system and method of the present disclosure or doneseparately from the system or method. This electronic plan is thenforwarded to a templating stage, which is done by suitableauto-templating software, but can be done partially or fully manually.The auto-templating software can be continuously or regularly updatedfrom other cases, which have been sent back for their data to be storedin the system. The analysis of the aggregated preoperative plansproduces an optimal preoperative plan for this patient's set of data.The goal of this optimal preoperative plan is to best apply the existingrules for preparing a preoperative plan for this particular patient'sanatomy. The preoperative plan is then reviewed by the surgeon. Thesurgeon may decide no further changes are necessary preoperatively orthe surgeon may decide to make any number of changes. Examples ofchanges made to the initial plan include, changes in the coronalalignment angle of the femoral or tibial components, change in the sizeof the components, changes in the sagittal alignment angle of thefemoral or tibial components, changes in the transverse plane alignmentrotational angle of the femoral or tibial components, translating thecomponent anteriorly, posteriorly, proximally or distally, removing moreor less bone during the preparation. These changes are recorded and sentback to the system to be stored and used to inform the system fordecision making on future cases. This feedback loop improves futurecases with similar preoperative data profiles. Next in the operation,prior to making definitive preparations to the bones of the knee, thefemur, the tibia, and the patella, the knee is evaluated by assessingits range of motion, if it has a deformity, whether that deformity ispassively correctable, completely or partially or none at all. Thesystem then recognizes these inputted changes and makes further changesto the preoperative plan based of these conditions seen in the operatingroom, while keeping the planning consistent with the existingpreoperative planning rules. These changes are recorded and sent back tothe system to be stored and used to inform the system for decisionmaking on future cases. This feedback loop improves future cases withsimilar preoperative data profiles and intra-operative data. The surgeonthen begins the preparation of the bones in the knee for implantation ofthe knee implants or components. The surgeon may need to make furtheradjustments to the preoperative plan as the surgeon sees the resultsbeing produced. If further changes are needed, these changes arerecorded and sent back to the system to be stored and used to inform thesystem for decision making on future cases. This feedback loop improvesfuture cases with similar preoperative data and intra-operativeprofiles. After the operation has been concluded, there are no furtherchanges to this patient's preoperative plan in this example. However,this patient's clinical outcome and images will be followed at variousintervals (e.g., at 6 weeks, 3 months, 1 years and annually throughoutthe patient's life). These outcomes are recorded and sent back to thesystem to be stored and used to inform the system for decision making onfuture cases. This feedback loop improves future cases with similarpreoperative data profiles.

In another example embodiment, a patient has decided to have a totalknee replacement performed. The surgeon caring for this patient prefersto employ the system and method of the present disclosure to useanalysis of one or more previous preoperative plans to produce the bestor better patient outcome scores for a current patient. As part of thepreparation for surgery, the patient has x-rays performed on the limband a CT Scan. Those electronic imaging studies undergo segmentation toprovide identification of boundaries and features as well as identifyingspecific anatomical features producing an electronic plan. In thisexample, this can be done by suitable auto-segmentation software or canbe performed partially or fully manually. This can be part of the systemand method of the present disclosure or done separately from the systemor method. This electronic plan is then forwarded to a templating stage,which is done by suitable auto-templating software but can be donepartially or fully manually. The auto-templating software can becontinuously or regularly updated by machine learning from other caseswhich have been sent back for their data to be stored the system. Theanalysis of the aggregated plans produces an optimal preoperative planfor this current patient's set of data. The goal of this optimalpreoperative plan is to produce best patient outcome scores. Thepreoperative plan is then reviewed by the surgeon. The surgeon maydecide no further changes are necessary preoperatively or the surgeonmay decide to make any number of changes. These changes are recorded andsent back to the system to be stored and used to inform the system fordecision making on future cases. This self-learning based on thisfeedback loop improves future cases with similar preoperative dataprofiles. Next in the operation, prior to making definitive preparationsto the bones of the knee, the femur, the tibia, and the patella, theknee is evaluated by assessing its range of motion, if it has adeformity, whether that deformity is passively correctable, completelyor partially or none at all. The system and method then recognizes theseinputted changes and makes further changes to the preoperative planbased of these conditions seen in the operating room. These changes arerecorded and sent back to the system to be stored and used to inform thesystem for decision making on future cases. This self-learning based onthese feedback loops improves future cases with similar preoperativedata profiles and intra-operative data. The surgeon then begins thepreparation of the bones in the knee for implantation of the kneeimplants. The surgeon may need to make further adjustments to thepreoperative plan as the surgeon sees the results being produced. Iffurther changes are needed, these changes are recorded and sent back tothe system to be stored and used to inform the system for decisionmaking on future cases. This self-learning based on this feedback loopimproves future cases with similar preoperative data and intra-operativeprofiles. After the operation has been concluded, there are no furtherchanges to this patient's preoperative plan in this example. However,this patient's clinical outcome and images will be followed at variousintervals (e.g., at 6 weeks, 3 months, 1 years and annually throughoutthe patient's life). These outcomes are recorded and sent back to thesystem to be stored and used to inform the system for decision making onfuture cases. This self-learning based on this further feedback loopimproves future cases with similar preoperative data profiles.

In another example embodiment, a patient is having a total kneereplacement. One standard alignment strategy for a total knee that asurgeon typically uses is mechanical axis strategy. However, the systemand method recognizes that this particular patient has a profile thatincludes a markedly valgus femur, a varus tibia with anterior bowing, avery hypotrophic lateral femoral condyle, and an increase in posteriortibial slope. The system and method (based on the feedback loop selflearning and in certain embodiments machine learning) uses theaggregated data of previous preoperative plans of similar preoperativedata cases, revisions of those similar plans, and the clinical outcomeof those similar patients, and produces a preoperative plan that differsfrom traditional mechanical axis strategy. The system and methodrecommends following the patient's anatomic alignment for implantplacement except internally rotating the femoral component two degreesand decreasing the tibial slope to three degrees. This produces apreoperative plan that the surgeon makes no preoperative changes to. Inthe operating room, no further changes are necessary, decreasing theoperative time. In doing so, this limits the time the patient has to beunder anesthesia, this decreases cardiac and pulmonary risk, thisshortens the time the wound is open, and decreases the risk ofinfection. This enables the patient to be more likely to recuperatefaster and be more likely to have a good result.

In various embodiments of the present disclosure, the third automatedfeedback loop is used to automatically create an enhanced future initialpreoperative plan and an enhanced virtual model based on aggregate denhanced preoperative plans and models. In various embodiments of thepresent disclosure, aggregating the results of the enhanced preoperativeplans includes automatically aggregating the results of the enhancedpreoperative plans using one or more machine learning systems orstrategies, or one or more model aggregation systems or strategies (suchas but not limited to a Statistical Natural Language Processing (SNLP),and/or Bayesian model aggregation system or strategy).

In various embodiments, the preoperative plans created and enhanced bythe system and method of the present disclosure can include, but are notlimited to, any of the following procedures: (a) orthopedic procedures;(b) knee replacement procedures; (c) hip replacement procedures; (d)partial knee replacement procedures; (e) partial hip replacementprocedures; (f) hip resurfacing procedures; (g) elbow replacementprocedures; (h) shoulder replacement procedures; (i) ankle replacementprocedures; (i) foot surgery; (j) hand surgery; (k) spine surgery; (l)fracture surgery; (m) reconstructive surgery of joints; (n) cartilagereconstruction procedures; (o) general surgery procedures; (p)cardiovascular procedures; (q) urologic procedures; (r) neurosurgicalprocedures; (s) abdominal surgery; (t) thoracic surgery; and (u)otolaryngological procedures.

While example embodiments of the present disclosure are discussed hereinrelated to surgical procedures, it should be appreciated that the systemand method of the present disclosure can be employed for: (a)non-robotic surgical procedures; (b) robotic assisted surgicalprocedures; (c) semi-autonomous robotic surgical procedures; and (d)autonomous robotic surgical procedures.

In various embodiments of the present disclosure, the system and methoddetermines an initial preoperative plan for an individual patient byautomatically analyzing that individual patient's data and comparingthat to aggregated stored patient data to find a best storedpreoperative plan and model for that individual patient's preoperativecondition. The system and method utilizes the aggregated data to producean optimized preoperative plan for that set of individual patientconditions. In various embodiments, the system and method uses one ormore statistical methods including, but not limited to, analysis ofvariance to select certain preoperative plans (or features thereof) andmodels. The system and method of the present disclosure can also usemachine learning software or strategies to solve for a designatedoptimal outcome, such as to provide for the fewest number of subsequentchanges to the preoperative plan or best patient reported outcome.

In various embodiments of the present disclosure, the system and methoduses or employs electronic measurement data obtained from or based onone or more series of measurements taken from electronic instrumentssuch as, but not limited to electronic sensors, or electronic imagingmachines (e.g., x-ray, MRI, etc) that fully or partially automaticallymeasure one or more characteristics of an individual patient feature.

It should be appreciated that in various embodiments, the system andmethod of the present disclosure determines and revises preoperativeplans based on one of the following or a combination of: (a) one or morestatistical aggregation methods, (b) predefined preoperative plan setanalysis techniques, and/or (c) machine learning and model specificationstrategies.

In various embodiments, the system and method of the present disclosuretakes as inputs a collection of disparate patient information, includingbut not limited to, a plurality of the following: (1) age, (2) sex, (3)nationality (to take into account the known distinctly differentpatterns of anatomy in different ethnic groups), (4) bone density, (5)height, (6) weight, (7) BMI, (8) lower extremity mechanical alignment,(9) lower extremity anatomical alignment, (10) femoral articular surfaceangle, (11) tibial articular surface angle, (12) mechanical axisalignment strategy, (13) anatomical alignment strategy, (14) naturalknee alignment strategy, (15) femoral bowing, (16) tibial bowing, (17)patello-femoral alignment, (18) coronal plane deformity, (19) coronalplane deformity that can be passively correctable, (20) sagittal planedeformity, (21) extension motion, (22) flexion motion, (23) ACL ligamentintact, (24) PCL ligament intact, (25) knee motion in all three planesduring active and passive range of motion in the joint; (26) threedimensional size, proportions and relationships of joint anatomy in bothstatic and motion, (27) height of the joint line, (28) lateralepicondyle, (29) medial epicondyle, (30) lateral femoral metaphysealflare, (31) medial femoral metaphyseal flare, (32) proximaltibio-fibular joint, (33) tibial tubercle, (34) coronal tibial diameter,(35) femoral interepicondylar diameter, (36) femoral intermetaphysealdiameter, (37) sagittal tibial diameter, (38) posterior femoral condylaroffset-medial and lateral, (39) lateral epicondyle to joint linedistance, and/or (40) tibial tubercle to joint line distance. The systemand method of the present disclosure may use machine vision systems toinput or obtain certain of the data for the system to use.

In various embodiments, the system and method of the present disclosureattempts to find a best fit for these preoperative data points in theuniverse of the previously collected and aggregated stored preoperativeplans.

Referring now to the drawings, FIG. 1 generally shows a system 100work-flow generally depicting an information feedback loop drivingpreoperative plan creation or design, in accordance with one exampleembodiment of the present disclosure. The system 100 includes or storesa set 110 of initial preoperative plans such as knee replacement surgerypreoperative plan 120. Electronic sensors and imaging capture data foreach of the preoperative plans 110 of the set that are aggregated tocreate or design a plurality of preoperative plans for initialdissemination. The output or preoperative set 130 (including initialpreoperative plan 130 a, secondary preoperative plan 130 b, and tertiarypreoperative plan 130 c) of preoperative plans and patient outcomeinformation (130 d) provides data for the aggregate set 140. Theaggregate set 140 aggregates the results from the further set ofpreoperative plans 130. In various embodiments, the system and methodperforms an integrated or comparative analysis 160 that compares theaggregate 140 to each individual model of the plurality of preoperativeplans. In various embodiments, the system and method performs adiagnostic analysis 170 to provide evidence to help understand whichpreoperative plans are optimal for each set of conditions found inpreoperative plans. This information is used to refine designs of thefuture preoperative plans using a feedback loop 150.

FIG. 2 is a flowchart 200 of a method of creating or designingpreoperative plans in a feedback loop system in accordance with oneembodiment of the present disclosure. As indicated by block 210, severalpreoperative plans are selected for creating or designing a preoperativeplan. As indicated by block 220, the results of the preoperative plansare aggregated. As indicated by block 230, each model analyzed iscompared to the aggregate result to obtain reliable or comparativeinformation. As indicated by block 240, the information is provided backto the system for the plurality of preoperative plans through thefeedback loop to create or design more accurate future preoperativeplans.

FIG. 3 shows a workflow depicting a feedback loop showing the variousstages of iteration available to a given preoperative plan and how eachiteration can be sent back to the aggregate independently or as part ofa complete workflow. Patient imaging and data 100, is collected andimages and/or data are sent via a data network (such as the internet) tothe system (which can include a cloud based server) as indicated byblock 110. That imaging and/or data is the processed and templating isperformed as indicated by block 200. Auto-templating identifies keyanatomic landmarks and applies rules to positioning of implant asindicated by block 200. This templating may be informed by machinelearning to optimize for fewest number of later revisions topreoperative plan needed and for the best clinical outcome with fewestcomplications as indicated by block 200. This process produces aninitial preoperative plan 210. The system and method forwards thispreoperative plan 210 to the surgeon for review as indicated by block300. The surgeon can approve or modify the initial preoperative plan 210to create a secondary preoperative plan 310. This secondary preoperativeplan 310 is sent back to or maintained by the system via a feedback loopto enable machine learning to optimize future preoperative plans asindicated by block 301. In addition, the secondary plan 310 is forwardedfor use in the operating room. In the operating room, the surgeon canmake or input further alterations to secondary plan based onintra-operative conditions (e.g., range of motion of the joint, angulardeformity, and ability to passively correct deformity) as indicated byblock 400 thereby creating a tertiary plan, 410. The tertiary plan 410is sent back to the system for saving via a feedback loop to enablemachine learning to optimize the preoperative plan, for future plans asindicated by block 401. In addition, after a preoperative plan has beenexecuted in the operating room, patient results are collected bystandardized outcome instruments, by patient reported outcomes, bysensors or by physician assessment as indicated by block 500. Theseassessments may include reports on complications, pain, range of motion,stability of the joint, longevity of the implant. This data may becollected by various mechanisms and at multiple locations. This patientoutcome information is sent back to the aggregate via another feedbackloop to enable machine learning to optimize the preoperative plan forthe creation of future preoperative plans as indicated by block 501.

It should be appreciated that various example embodiments of the presentdisclosure provide a system which generally includes one or morecomputers (such as one or more servers and one or more memory devicesthat store one or more databases) configured to communicate with one ormore electronic access devices. For purposes of simplicity and brevity,this disclosure primarily describes the system and method of the presentdisclosure with respect to one surgeon and one patient. However, itshould be appreciated that the system and method of the presentdisclosure is meant to simultaneously work with many surgeons and manypatients. The system and method of the present disclosure is expandableto large quantities of surgeons and patients. It should be appreciatedthat, for implementation with multiple different surgeons and patients,the system and method of the present disclosure will provide suitablesecure segregation of patient data for patient privacy and security inaccordance with applicable privacy and other legal or regulatoryrequirements (such as HIPAA compliance).

In various embodiments, the system includes one or more computers suchas one or multiple servers configured to communicate through a datanetwork (such as the internet) with a plurality of electronic accessdevices. In certain embodiments, the system is also configured tocommunicate with one or more EHR or EMR systems. The computer(s) (suchas the server(s)) includes one or more central processing units (notshown) and one or more memory devices (not shown) which storeinstructions (not shown), and one or more databases (not shown). Itshould be appreciated that computers or servers employed in the systemof the present disclosure may have various configurations and may becloud based. The present disclosure contemplates that the access devicescan include any suitable user computer and/or computerized electroniccommunication device. Such devices include, but are not limited to: (a)a cellular telephone (such as a smart phone); (b) a tablet-computingdevice; (c) a laptop computer; and (d) a desktop computer. Users operatethese access devices to access or communicate with the system throughthe data network.

It should be appreciated that in various embodiments, the systemoperates with the access devices through one or more software programsor applications downloaded to those access devices (i.e., commonlycalled “apps”). It should be appreciated that in other variousembodiments, the system operates with the access devices through one ormore software programs or web sites accessible by those access devices.It should be appreciated that in various embodiments, the systemoperates with the access devices through one or more software programsor applications downloaded to those access devices and through one ormore software programs or web sites accessible by those access devices.It should be further appreciated that the system and the user accessdevices can co-act in other suitable manners in accordance with thepresent disclosure.

In various embodiments, the system requires each of the users toregister with the system. In such embodiments, each user has a useridentifier (such as a name or e-mail address) and a password or otheridentifier to access or use the system.

In various embodiments, the system communicates with the various accessdevices through one or more suitable wired, partially wired, or wirelessdata networks. Thus, it should be appreciated that the system of thepresent disclosure can operate through a suitable central or remotenetwork such as but not limited to one or more local area networks(LANs), one or more wide area networks (WANs), one or more cellularnetworks, one or more intranets, and/or the internet.

In various embodiments, the system of the present disclosure suitablystores data needed to operate the system in one or more suitable systemdatabases. It should be appreciated that any other suitable databaseschemes and relationships can be employed in accordance with the presentdisclosure.

It should be appreciated that the system suitably stores data regardingeach of patients in suitable system databases. In various embodiments,all such patient data and communications regarding patient data areencrypted. For example, in certain embodiments, the system encrypts(using 256-bit AES encryption) all patient related data at rest, intransit and in use. It should be appreciated that the system of thepresent disclosure can encrypt the patient data in other suitablemethods.

It should be understood that modifications and variations may beeffected without departing from the scope of the novel concepts of thepresent invention, and it should be understood that this application isto be limited only by the scope of the claims.

In some examples, the aggregation of preoperative plans, measurements,data, secondary preoperative plans, tertiary preoperative plans, finalplans and/or outcome measures of surgical procedures creates data whichmay be analyzed, over a server, on a local electronic access device,electronic surgical device, or surgical robot. In some examples, thedisclosed systems and methods may analyze data by methods including butnot limited to, Statistical Natural Language Processing, Bayesianaggregation, Machine learning, artificial intelligence, self-learning,Neural Networks, Deep recurrent neural networks, basic reinforcementlearning, and/or deep reinforcement learning. In some examples, data,such as preoperative data or intra-operative data, may include datarelating to soft tissue, such as range of motion, angular deformities,flexion contracture, recurvatum, limited flexion, varus, valgus, and theability to passively correct the deformity, joint flexibility and/orlaxity. In some examples, pre-operative data may be any informationobtained by the surgeon, a surgical robot, the server, the electronicaccess device or any other means relating to the patient or surgicaloperation during the performance of the surgery. In some examples,intra-operative data may include information obtained immediately beforeor immediately after the performance of the surgery, such as datarelating to preparation of the patient for surgery or measurements ofthe patients anatomy made soon after the completion of the surgery. Inaddition to those discussed above, preoperative data, intra-operativedata and/or postoperative data, such as patient data and/or outcomedata, may include Electronic Medical Records (EMR) data, data from theKnee Society Clinical Rating System (KSS), data from the Western Ontarioand McMaster Universities Osteoarthritis Index (WOMAC), Hoos, Koos,SF-12, SF-36, Harris Hip Score, patient self reported data, biometricdata, data from wearable sensors, and/or data from imaging techniquessuch as Magnetic Resonance Imaging (MRI), radiography, ultrasound,x-ray, thermography, tactile imaging, elastography, nuclear medicinefunctional imaging, positron emission tomography (PET), single-photonemission computer tomography (SPECT), computed tomography scanning (CTscanning), or other types of medical imaging. In some examples, imagedata may enable surface mapping of a patient's anatomy, surgicalimplant, or surgical instrument. Image data may be acquiredpreoperatively, intra-operatively, or postoperatively. In some examples,the system and/or method may incorporate augmented reality applicationssuch as a virtual model using medical images that utilized real-timesurgical data to create an augmented reality using a surgical robot orelectronic access device. In other examples, the system and/or methodmay not include an imaging system.

In some examples, the system or method may include gathering or usingpatient alignment data such as mechanical axis alignment, anatomicalignment, natural knee alignment, gap balancing, measured resection,and other forms of bodily alignment in preparing preoperative plans,implant designs, medical instrumentation designs, or otherrecommendations. In some examples, the system or method may includegathering genomic data of the patient or of other patients, such aspatients who share at least one common feature with a subject patient.In addition to those noted above, the system or method may be used forosteotomy procedures, computer navigated surgery, neurological surgery,spine surgery, otolaryngology surgery, orthopedic surgery, generalsurgery, urologic surgery, ophthalmologic surgery, obstetric andgynecologic surgery, plastic surgery, valve replacement surgery,endoscopic surgery, and/or laparoscopic surgery.

In some examples, the system or method may enable optimization ofplacement of implants of autologous tissue, allograft tissue, and/orsynthetic materials. In some examples, the system or method may enablethe optimization of implant choice and/or implant placement within thebody of a patient. For example, the system or method may select, basedon data from previous patients who shares at least one common featurewith the subject patient, an implant or implant design to match theanatomy of a patient, such as matching the arc of curvature of thenative femoral condyle with the implant arc of curvature. In someexamples, the system or method may generate a custom knee implantdesign, custom hip implant design, custom partial knee or hip implantdesign, or custom design of any other implant design for any other partof a patient's anatomy.

In some examples, the system or method may be used with a roboticsurgical system in robotic assisted surgical procedures; (c)semi-autonomous robotic surgical procedures; (d) autonomous roboticsurgical procedures; or (e) manually controlled robotic surgicalprocedures. For example, the system may include a surgical robotincluding a robotic arm, which may be operated by a surgeon,semi-autonomously, autonomously, or a combination thereof. In someexamples, a surgical robotic system may be used in the systems andmethods discussed herein in any of the applications described herein.For example, a surgical robot may display or provide suggestions to asurgeon or other health care provider, via a server, an electronicaccess device, or the surgical robotic system, regarding changes to theoperative plan during the surgical procedure based on intraoperativedata collected during the surgical procedure. In some examples, thesystem or method may be used with a surgical robotic system including arobotic arm configured for partial knee replacement, partial hipreplacement, total knee replacement, and/or total hip replacementsurgery. A surgical robotic system may include a conventional server, anelectronic display, one or more electronic access devices (such as oneor more computers), and/or one or more surgical robots.

In some examples, the system and/or method may include the use of asurgical robotic system that includes a haptically controlled robot. Inother examples, a surgical robotic system may include one or morehaptically controlled features, such as selecting icons on a display ordownloading data from a server. For example, the surgical robotic systemmay include a haptic device such as the Tactile Guidance System™ (TGS™)manufactured by MAKO Surgical Corp., and used to prepare the surface ofa patient's bone for insertion of an implant. A haptic device mayprovide haptic or tactile guidance to guide a surgeon during a surgicalprocedure. In some examples, a haptic device may be an interactivesurgical robotic arm that holds a surgical tool, such as a surgicalburr, and is manipulated by the surgeon to perform a procedure on apatient, such as cutting a surface of a bone.

A surgical robotic system, such as any of the computer or roboticsystems described herein, may store any changes made to a preoperativeplan preoperatively, intraoperatively, or postoperatively. For example,if a surgeon updates a operative plan to account for a particular softtissue condition of a patient discovered while conducting the surgicalprocedure, the surgical robotic system will store this update and/orsend this update to a server to be stored. In some examples, thesurgical robotic system will automatically store any changes made to apreoperative plan, whether the changes are made by a surgeon, by anelectronic access device, by a server, by a the surgical robotic system,or any other changes made preoperatively, intraoperatively, orpostoperatively.

In some examples, a surgical robotic system may include a robotic armand an electronic display, and may generate a three-dimensional virtualmodel of the surgical procedure and display the three-dimensionalvirtual model on the electronic display. In other examples, the systemor method may generate a two-dimensional virtual model or a combinationof a two-dimensional and a three-dimensional virtual model of apatient's anatomy. In some examples, intra-operative adjustments to avirtual model may be made by the surgical robotic system, such as by arobotic device, a robotic arm, a electronic user interface, or a server.In some examples, kinematic and soft tissue data may be collected usingthe surgical robotic system and a virtual model may be updated based onsuch intra-operative data. In some examples, a robotic surgical systemmay include a robotic arm that may cut, ablate, bur, or move a patient'sanatomy or surgical implant, and may be configured to place an implantwithin a patient's anatomy. In some examples, the system or method mayinclude generating a preoperative plan for a surgical robot includingsteps for cutting, ablating, burring, or moving a patient's anatomy witha surgical robot.

In some examples, a robotic surgical system may include generating avirtual model of a patient's anatomy. During a surgical procedure, thedisclosed system or method may include a device for measuring positionson a patient's anatomy to modify a virtual model of the patient'sanatomy and such a device may be operated by a robotic device or asurgeon.

Any of the systems and methods discussed herein may include a test orevaluation to be completed before the operative plan can be finalized.Such a test or evaluation may be displayed using an electronic accessdevice, a surgical robotic system, a server, or other electronic deviceand may require the surgeon to complete the test before beginning orcontinuing a surgical procedure based on an operative plan generated bythe system or on data acquired preoperatively or intraoperatively. Forexample, the system may indicate to the surgeon that one or moreanatomical measurements need to be made (such as range of motion,ligament tension, cartilage disease). In such an example, once thesurgeon and/or surgical robotic system completes the test, the surgicalprocedure may continue and the surgeon or surgical robotic system mayexecute the operative plan. In some examples, the system and/or methodmay require the surgeon and/or robotic surgical system to complete anassessment of the patient's soft tissue (before, during, or after thesurgical procedure), and the system may then confirm or adjust theoperative plan based on the results of the soft tissue assessment. Inother examples, the system or method may generate a test for the surgeonto complete or prompt the surgeon and/or robotic surgical system toevaluate the patient when a particular threshold patient measurementvalue is reached, such as prompting or suggesting an intraoperative testfor the surgeon to complete when a patient's heart rate exceeds athreshold. In some examples, the system and/or method may automaticallycreate a new preoperative plan and/or new virtual model when a thresholdpatient measurement value is reached. In this example, the thresholdpatient measurement value may be any kind of patient measurementdescribed herein and may be measured by a health care provider such as asurgeon, a surgical robotic system, an imaging system, or any othermeans.

In some examples, the system and/or method may prompt a surgeon, such asthrough a visual icon or notification displayed using a robotic surgicalsystem, to conduct a test or evaluation of the patient's anatomy. Basedon the information from the results of the test or evaluation, thesystem and/or method may affirm, suggest a change to or automaticallyupdate the operative plan.

In some examples, the system and/or method may tie or correlateintraoperative measurements with preoperative measurements. For example,the system and/or method may generate an indication for the surgeon orthe robotic system to complete a range of motion test on the patientprior to surgical procedure so that the range of motion test is notrequired intraoperatively. In some examples, the system may correlatespecific measurements of an anatomical structure in the patients body tothe planned position of an implant with the same measurement fromprevious patients who shares at least one common feature with theindividual patient to predict potential intraoperative plan adjustmentsthat may be required.

In one example, the system and/or method may be used to generate apreoperative plan for a soft tissue procedure, such as a meniscal repairor removal, tissue tear repairs, oblation, tumor resections, etc.Preoperative data, such as medical imaging data, may indicate that thepatient has a soft tissue tear. After the surgical procedure iscompleted, the system and/or method may record and store in a server, asurgical robot, and/or electronic access device the results of theprocedure to correlate the surgical technique with the patient's outcomedata. For example, the surgical procedure may include a manuallyoperated robot that records the motions and tills used to complete thesurgical procedure, and based on the patient's outcome data, the systemmay recommend particular motions and tills of the manually operatedrobot when generating a surgical plan for a similar soft tissue tearprocedure. In another example, the system and/or method may generate apreoperative plan for a tumor resection procedure and may require, atany point prior to or during the procedure, a measurement of surroundingtissue to confirm resection boundaries are appropriate.

Embodiments of the subject matter described in this disclosure may beimplemented using computer assisted procedures. For example, a computerassisted procedure may include a computer system, surgical roboticsystem, and a navigation system that the surgeon may use as a tool tocomplete the surgery. The computer assisted surgery may include anelectronic display that may provide visual suggestions to the surgeonpreoperatively and/or intraoperatively. For example, a computer assistedsurgery may include an imaging system with a display that providesguidance to the surgeon regarding the positioning of the patient,surgical instruments, one or more prosthetics, or a surgical robot. Insome examples, an electronic access device and/or a server may be usedin a computer assisted procedure. A computer assisted surgery mayinclude a computer program that incorporates systems and/or methodsdescribed herein.

In some examples, the system and/or method may generate a virtualboundary and incorporate the virtual boundary into the preoperativeplan. A virtual boundary may be generated using a surgical roboticsystem, a medical imaging system, a server, a electronic access device,and/or by using data from prior patients who shares at least one commonfeature with the individual patient. A virtual boundary may indicate anarea of the patient to be resected, such as an area of soft tissue orbone to be resected, or an area to avoid resecting. In some examples, avirtual boundary may be overlaid on a CT scan dataset (or othernon-segmented data) and may be created using data from an implant. Avirtual boundary may identify a volume, axis, or plan to resect in orderto position an implant.

The systems and methods of the present disclosure may includemeasurements from implantable sensors that may provide data regardingthe patient preoperatively, intraoperatively, or postoperatively. Insome examples, an implantable sensor may measure force, torque,displacement, and/or blood flow. In some examples, an implantable sensormay measure prosthetic or implant degradation or structural integrity,position of implant, data relating to the patient's tissue environment,or movement of an implant within the body of the patient. In someexamples, implants may be parts of a joint replacement, fracture repairplate, nail, screw fixation, suture, bone graft, ligament graft, orother type of implant.

In some examples, the system and/or method may be used in a ligamenttransfer procedure. For example, the system and/or method may includeusing a surgical robotic system to transfer a ligament and the surgicalrobotic system may record the steps of the procedure. The recorded stepsmay be compared with patient postoperative data to indicate success orfailure of a procedure, and the system and/or method may associate thesteps of the ligament transfer procedure with the recorded patientoutcome data for use in creating a preoperative plan for a subsequentpatient who shares at least one common feature with the individualpatient. In some examples, the postoperative data of the patient mayinclude outcome data relating to particular activities of a patient,such a whether the patient is able to walk after a certain amount oftime after a total knee replacement.

In some embodiments of the present disclosure, the system and/or methodmay generate a preoperative plan that includes details regarding thesurface preparation of a patient's bone for positioning of an implant.For example, the preoperative plan may include preparation of thepatient's bone to create a specific surface texture to facilitateplacement of an implant. In some examples, the system and/or method mayuse data from prior patients who share at least one common feature withthe individual patient to generate the individual patient's preoperativeplan, such as the surface texture used in a prior patient's procedureand the tendency for an implant to loosen or shift positions within apatient's body after completion of the surgical procedure.

We claim:
 1. A computer-implemented method for optimizing a futuresurgical preoperative plan, said method comprising: providing, via aserver, an electronic initial preoperative plan for a robotic surgicalprocedure, wherein the surgical procedure includes cutting tissue forreceiving an implant and/or prosthesis with the assistance of a surgicalrobot; receiving at the server or the electronic access device, prior tothe surgical procedure, information regarding the initial preoperativeplan from the surgical robot including information related to a priormovement of a robotic arm to assist in preparing a bone for implantand/or prosthesis insertion; executing an algorithm, via the server, tocreate a secondary preoperative plan incorporating the information fromthe surgical robot; executing the secondary preoperative planincluding 1) moving a robotic arm of the surgical robot to cut, ablate,bur, or move a patient's anatomy; and/or 2) moving a robotic arm of thesurgical robot to move the implant or prosthesis; storing in the serverand/or the electronic access device the information from the surgicalrobot for use in creating a preoperative plan for a subsequent patientwho shares at least one common feature with the individual patient;receiving at the server and/or the electronic access device, during thesurgical procedure for the individual patient and via the surgicalrobot, patient surgical information; and storing in the server and/orthe electronic access device the patient surgical information for use increating a preoperative plan for a subsequent patient who shares atleast one common feature with the individual patient.
 2. The method ofclaim 1, further comprising: executing an algorithm using the patientsurgical information, via the server and during the surgical procedurefor the individual patient, to create a tertiary preoperative plan;displaying, via the electronic access device or the surgical robot, thetertiary preoperative plan to the surgeon during the surgical procedure;and storing in the server and/or the electronic access device thetertiary preoperative plan for use in creating a preoperative plan for asubsequent patient who shares at least one common feature with theindividual patient.
 3. The method of claim 1, wherein the server ispositioned within the surgical robot.
 4. The method of claim 1, whereinthe surgical robot includes a robotic assisted device or surgeonassisted device, a computer assisted device, an autonomous roboticdevice, or a digital surgery platform.
 5. The method of claim 1, furthercomprising: receiving, after the surgical procedure and via the server,the surgical robot, or the electronic access device, informationregarding the results of the surgical procedure from the patient;executing an algorithm, via the server, surgical robot, or electronicaccess device, to create an updated preoperative plan incorporating theinformation regarding the results of the surgical procedure from thepatient; and storing in the server and/or the electronic access devicethe updated preoperative plan for use in creating a preoperative planfor a subsequent patient who shares at least one common feature with theindividual patient.
 6. The method of claim 1, further comprising:executing an algorithm, via the server, to create a virtual model of thepatient's anatomy incorporating the information from the surgeon orsurgical robot and representing an application of the secondarypreoperative plan; providing, via the server and the electronic accessdevice, the virtual model to the surgical robot or the electronic accessdevice; displaying, via the electronic access device or the surgicalrobot, the virtual model to the surgeon during the surgical procedure;and storing in the server and/or the electronic access device thevirtual model for use in creating a virtual model for a subsequentpatient who shares at least one common feature with the individualpatient.
 7. The method of claim 6, wherein displaying the virtual modelto the surgeon includes displaying an augmented reality of the surgicalprocedure.
 8. The method of claim 1, wherein the algorithm includes anaggregation of preoperative plans, surgical measurements, and patientoutcomes stored on the server from prior surgical procedures involvingpatients who share at least one common feature with the individualpatient.
 9. The method of claim 8, where the algorithm includes analysisof preoperative plans, surgical measurements, and patient outcomesstored on the server from prior surgical procedures involving patientswho share at least one common feature with the individual patient,wherein the analysis includes using Statistical Natural LanguageProcessing (SNLP), Bayesian aggregation, Machine learning, artificialintelligence, self-learning, Neural Networks, Deep recurrent neuralnetworks, basic reinforcement learning, and deep reinforcement learning.10. The method of claim 1, wherein the patient surgical informationincludes at least one of the patient's range of motion, patient'sanatomical alignment, joint gap distance, implant or prostheticposition, ligament tension, one or more knee joint forces, soft tissueconditions, angular deformities, flexion contracture, recurvatum,limited flexion, varus, valgus, joint flexibility, laxity, or ability topassively correct a deformity.
 11. The method of claim 5, wherein theinformation regarding the results of the surgical procedure from thepatient includes electronic medical records (EMR) data, data related tothe knee society clinical rating system (KSS), data related to theWestern Ontario and McMaster Universities osteoarthritis index (WOMAC),HOOS, KOOS, SF-12, SF-36, Harris Hip Scores, wearable sensor data,patient self-reported data, or imaging data from medical images of thepatient.
 12. The method of claim 1, further comprising: providing, viathe server and the electronic access device, suggestions to modify thesecondary preoperative plan to the surgical robot during the surgicalprocedure based on the patient surgical information, wherein the patientsurgical information includes data obtained during the patient'soperation relating to the patient's soft tissue and may include range ofmotion, patient's anatomical alignment, joint gap distance, implant orprosthetic position, ligament tension, one or more knee joint forces,deformity, correctability of deformity, measured resection, mechanicalaxis, natural knee alignment, or gap balancing.
 13. The method of claim1, wherein selecting an electronic initial preoperative plan for arobotic surgical procedure includes receiving and analyzing, via theserver, genomic data of the patient or surface mapping data from imagesof the patient's anatomy or by direct mapping of the patient's tissuetopography during the procedure by way of computer digitization, laserscanning, light scanning or ultrasound scanning.
 14. The method of claim3, further comprising: displaying, via the electronic access device orthe surgical robot, a real-time image of the patient's anatomy to thesurgeon during the surgical procedure; wherein the patient surgicalinformation includes medical imaging data of the patient.
 15. The methodof claim 1, wherein the patient information regarding the initialpreoperative plan from the surgeon or surgical robot including at leastone of: the patient's femoral articular surface angle, tibial articularsurface angle, femoral bowing, tibial bowing, patella-femoral alignment,coronal plan deformity, sagittal plane deformity, extension motion,flexion motion, ACL ligament integrity, PCL ligament integrity, kneemotion in all three planes during active and passive range of motion inthe joint, height of the joint line, lateral epicondyle, medialepicondyle, lateral femoral metaphyseal flare, medial femoralmetaphyseal flare, proximal tibio-fibular joint, tibial tubercle,coronal tibial diameter, femoral interepicondylar diameter, femoralintermetaphyseal diameter, sagittal tibial diameter, posterior femoralcondylar offset-medial and lateral, lateral epicondyle to joint linedistance, or tibial tubercle to joint line distance.
 16. The method ofclaim 1, wherein the surgical procedure includes: the placement of animplant of autologous tissue, allograft tissue, or synthetic materials;a cardiac, orthopaedic, neurologic, urologic, ophthalmologic, obstetricand gynecologic, otolaryngologic, plastic, or general surgicalprocedure; and/or a valve replacement or a transcatheter aortic valvereplacement (TAVR) procedure.
 17. A computer-implemented method foroptimizing a future surgical preoperative plan, said method comprising:providing, via a server, an electronic initial preoperative plan for asurgical procedure, wherein the surgical procedure includes cuttingtissue for receiving an implant and/or prosthesis with the assistance ofa surgical robot; creating, via the server, a patient specific implantdesign and/or prosthesis design; receiving at the server and/or theelectronic access device, prior to the surgical procedure and via thesurgical robot, information regarding the initial preoperative planand/or the implant design and/or prosthesis design; executing analgorithm, via the server, to create a secondary preoperative plan and asecondary implant design and/or prosthesis design incorporating thereceived information, wherein the secondary preoperative plan includes arecommended movement of a robotic arm to assist in preparing a bone forimplant and/or prosthesis insertion; storing in the server and/or theelectronic access device the information from the surgical robot for usein creating a preoperative plan and/or implant design and/or prosthesisdesign for a subsequent patient who shares at least one common featurewith the individual patient; identifying an implant and/or prosthesisfor use in the surgical procedure based on the secondary implant designand/or the secondary prosthesis design; executing the secondarypreoperative plan including: 1) moving a robotic arm of the surgicalrobot to cut, ablate, bur, or move a patient's anatomy; and/or 2) movinga robotic arm of the surgical robot to move the implant or prosthesis;and storing in the server and/or the electronic access device theidentified implant and/or prosthesis design for use in creating apreoperative plan and/or implant and/or prosthesis design for asubsequent patient who shares at least one common feature with theindividual patient.
 18. The method of claim 17, wherein creating thepatient specific implant design and/or prosthesis design includesmatching the patient's arc of curvature of the native femoral condylewith an arc of curvature of the implant and/or prosthesis.
 19. Themethod of claim 17, wherein creating a patient specific implant designand/or prosthesis design includes: creating the implant design and/orprosthesis design using medical images of the patient and an algorithmthat includes the aggregation of preoperative plans, surgicalmeasurements, medical images, and patient outcomes stored on the serverfrom prior surgical procedures involving patients who shares at leastone common feature with the individual patient.
 20. Acomputer-implemented method for optimizing a future surgicalpreoperative plan, said method comprising: providing, via a server, anelectronic initial preoperative plan for a robotic surgical procedure,wherein the surgical procedure includes cutting tissue for receiving animplant and/or prosthesis with the assistance of a surgical robot;receiving at the server and/or the electronic access device, prior tothe surgical procedure, information regarding the initial preoperativeplan from the surgical robot; executing an algorithm, via the server, tocreate a secondary preoperative plan incorporating the information fromthe surgical robot and including a recommended movement of a robotic armto assist in preparing a bone for implant and/or prosthesis insertion;storing in the server and/or the electronic access device theinformation from the surgical robot for use in creating a preoperativeplan for a subsequent patient who shares at least one common featurewith the individual patient; receiving at the server and/or theelectronic access device, during the surgical procedure for theindividual patient and via the surgical robot, patient surgicalinformation including data collected by the surgical robot related to atleast one of: soft tissue tension, ligament integrity, range of motionof a joint, or quality of articular cartilage; storing in the serverand/or the electronic access device the patient surgical information foruse in creating a preoperative plan for a subsequent patient who sharesat least one common feature with the individual patient; executing analgorithm using the patient surgical information, via the server andduring the surgical procedure for the individual patient, to create atertiary preoperative plan; displaying, via the electronic access deviceor the surgical robot, the tertiary preoperative plan to the surgeonduring the surgical procedure; executing the tertiary preoperative planincluding: 1) moving a robotic arm of the surgical robot to cut, ablate,bur, or move a patient's anatomy; and/or 2) moving a robotic arm of thesurgical robot to move the implant or prosthesis; storing in the serverand/or the electronic access device the tertiary preoperative plan foruse in creating a preoperative plan for a subsequent patient who sharesat least one common feature with the individual patient receiving at theserver, the surgical robot, and/or the electronic access device, afterthe surgical procedure, information regarding the results of thesurgical procedure; executing an algorithm, via the server, surgicalrobot, or electronic access device, to create an updated preoperativeplan incorporating the information regarding the results of the surgicalprocedure; and storing in the server and/or the electronic access devicethe updated preoperative plan for use in creating a preoperative planfor a subsequent patient who shares at least one common feature with theindividual patient.